[go: up one dir, main page]

WO2020181594A1 - Procédé de mesure quantitative d'une structure spatiale de matière organique particulaire du sol - Google Patents

Procédé de mesure quantitative d'une structure spatiale de matière organique particulaire du sol Download PDF

Info

Publication number
WO2020181594A1
WO2020181594A1 PCT/CN2019/080778 CN2019080778W WO2020181594A1 WO 2020181594 A1 WO2020181594 A1 WO 2020181594A1 CN 2019080778 W CN2019080778 W CN 2019080778W WO 2020181594 A1 WO2020181594 A1 WO 2020181594A1
Authority
WO
WIPO (PCT)
Prior art keywords
organic matter
soil
image
spatial structure
particulate organic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/080778
Other languages
English (en)
Chinese (zh)
Inventor
刘兴华
骆永明
章海波
谢寅雨
朱荣生
王诚
王怀中
呼红梅
黄保华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute Animal Science and Veterinary Medicine of Shandong AAS
Original Assignee
Institute Animal Science and Veterinary Medicine of Shandong AAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute Animal Science and Veterinary Medicine of Shandong AAS filed Critical Institute Animal Science and Veterinary Medicine of Shandong AAS
Priority to AU2019434881A priority Critical patent/AU2019434881A1/en
Publication of WO2020181594A1 publication Critical patent/WO2020181594A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/34—Purifying; Cleaning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]

Definitions

  • the invention relates to a method for quantitatively determining the spatial structure of soil particulate organic matter, and belongs to the technical field of soil research.
  • Soil particulate organic matter has complex physical structure characteristics, and there is currently no unified and effective method at home and abroad to intuitively and quantitatively determine the spatial structure of soil particulate organic matter.
  • Scholars at home and abroad use non-destructive microanalysis techniques, such as electron microscopy, scanning electron microscopy-energy spectrum analysis technology (SEM-EDX) and other means to study the surface morphology and chemical composition of soil particulate organic matter.
  • SEM-EDX scanning electron microscopy-energy spectrum analysis technology
  • the soil granular organic matter of different sources and degrees of humification shows obvious structural differences under the electron microscope, such as flocculent structure, layered structure and inter-flocculent structure.
  • the use of electron microscopy, scanning electron microscopy and other methods can only visually distinguish the differences in soil organic matter structure, while the quantitative analysis of organic matter spatial structure and the determination of morphological and structural parameters cannot be achieved.
  • the synchrotron radiation-based micro-computed tomography (macro-CT) technology can capture the detailed features of the soil structure through the conversion of light and electrical signals and perform quantitative determinations.
  • This method has the advantages of fast speed, strong imaging contrast and high resolution.
  • scholars at home and abroad mostly use this technology to study soil pore structure, distribution and preferential flow relationship, and the fractal characteristics of soil aggregates.
  • the method is continuously optimized and upgraded in the application process, it is gradually applied to the analysis of the microstructure characteristics of soil aggregates, such as the analysis of changes in the fractal structure of pores in soil aggregates. Flavel R J et al.
  • micro-CT technology has the advantages of faster and more accurate pore structure research (Flavel R J, Guppy C N, Tighe M, et al. Non-destructive quantification of cereal roots in soil using high-resolution X-ray tomography [J]. Journal of Experimental Botany:2012:421).
  • the present invention provides a method for quantitatively determining the spatial structure of soil particulate organic matter.
  • This method first extracts the particulate organic matter in the soil by wet sieving classification and density extraction, and then scans the particulate organic matter with micro-CT technology.
  • the scanned image is processed by removing artifacts-calculating threshold-image segmentation-three-dimensional reconstruction Analyze the process of structure and other processes to quantify the parameters of the soil's mechanical morphology, organic matter quantity, volume ratio and volume distribution, and pore size distribution.
  • the technical scheme of the present invention is: a method for quantitatively determining the spatial structure of soil particulate organic matter, which is characterized by including the following steps:
  • the scanned projection image is processed by removing artifacts, calculating thresholds and image segmentation, dividing the image into three parts: pores, organic matter and soil minerals, and then performing three-dimensional reconstruction to restore the original appearance of soil granular organic matter;
  • the wet sieve classification of the step 1) is as follows: add soil into the centrifuge tube and slowly add water to infiltrate it to avoid the rapid increase in water pressure and cause the destruction of the soil structure; then the centrifuge tube is placed upside down on a 2mm sieve (2mm sieve lower layer is placed in sequence and matched 250 ⁇ m and 53 ⁇ m sieve) below the water surface until the soil sample completely sinks into the sieve; move the sieve up and down, and classify through the wet sieve to obtain large aggregates with a particle size of 250-2000 ⁇ m and micro-aggregates with a particle size of 53-250 ⁇ m;
  • the density extraction in step 1) is:
  • the lower reorganized organic matter is dispersed with 5g/L sodium hexametaphosphate and then wet sieved to obtain the granular organic matter bound inside the macroaggregates (microaggregates);
  • the artifact removal, threshold calculation and image segmentation in the step 3) are: preprocessing removes the artifacts and then performs slice segmentation, outputs it as a binary image, converts the binary image into an octal image and performs threshold segmentation; the threshold is selected
  • the global threshold method is selected by observing the gray value histogram. The pores have no absorption of X-rays, the gray value is the smallest, and the soil mineral absorption is the largest. The gray value is larger, and the organic matter is between the two. Build a histogram with different gray values. The histogram will have two peaks. The gray value of the middle trough of the two peaks can be selected as the threshold to divide the image into three parts: pores, organic matter, and soil minerals.
  • the organic matter components and soil minerals can be dyed to enhance the visual contrast between the two.
  • step 4) quantitatively calculates the spatial structure characteristics of the organic matter using image J software, and the size of the organic matter pores is expressed in the form of equivalent diameter.
  • the present invention first extracts the particulate organic matter in the soil through wet screening and density extraction methods and grouping them, thereby solving the problem of "soil particulate organic matter is randomly distributed in the soil, complicated in structure, and relatively difficult to quantitatively determine”. Provides the possibility for the quantitative determination of soil particulate organic matter;
  • the present invention uses the micro-CT technology for the first time to quantitatively determine the spatial structure characteristics of soil granular organic matter (morphological characteristics, organic matter quantity, volume ratio and volume distribution, pore size distribution and other parameters). This is a soil science, especially soil organic matter.
  • the in-depth study laid the foundation.
  • Figure 1 is a micro-CT scan image of soil particulate organic matter
  • Figure 2 is the reconstructed slice of soil granular organic matter micro-CT scan image converted into octal image
  • Figure 3 is an example diagram of threshold analysis to distinguish between organic matter and soil minerals
  • Figure 4 is a distribution diagram of soil organic matter particles after segmentation and dyeing, where gray is the organic matter component and green is the attached mineral component;
  • Figure 5 is a 3D reconstruction image of soil granular organic matter whose volume pixels are 500 ⁇ 500 ⁇ 500.
  • the method of density extraction extracts particulate organic matter
  • Use NaI solution with a density of 1.85g/cm 3 to classify the density of macroaggregates and microaggregates that is, take 5g agglomerates sample in a 100mL centrifuge tube with a solid-to-liquid ratio of 1:7, upside down for 1 min, and let stand for 30 min Afterwards, the upper layer of light organic matter is separated by filtration, and the lower layer sample repeats the above steps until the light organic matter is completely separated, and free particulate organic matter (fPOM) of macroaggregates (250-2000 ⁇ m) and microaggregates (53-250 ⁇ m) is obtained.
  • fPOM free particulate organic matter
  • the lower reorganized organic matter was dispersed with 5g/L sodium hexametaphosphate for 16 hours and then wet sieved to obtain the particulate organic matter (iPOM) bound inside the agglomerate.
  • the samples of each component are marked as: fPOM (250-2000 ⁇ m), iPOM (250-2000 ⁇ m) ), fPOM (53-250 ⁇ m), iPOM (53-250 ⁇ m).
  • the granular organic matter of the fPOM (250-2000 ⁇ m) component is taken as an example; the micro-CT experiment analysis is carried out.
  • the micro-CT scanning imaging experiment of soil granular organic matter is in Shanghai Light Source BL13WX
  • the radiography beam line station is completed.
  • the sample scanning parameters are set as follows: photon energy is 18keV, resolution is 3.25 ⁇ m, the sample stage rotates at a constant speed from 0 to 180° in the horizontal direction, exposure time is 1.2s, a total of 1080 projection images are collected, and the CCD detector records scanning projections at various angles (The picture shown in Figure 1 is one of them). Then 1080 projections of each sample were used for reconstruction of CT images, and 1508 slices were obtained, and the resolution of each projection image was 2048 pixels ⁇ 2048 pixels.
  • the reconstruction of the internal structure of the sample uses a filtered back projection algorithm.
  • the image is divided into three parts: pores, organic matter and soil minerals.
  • Quantitative analysis mainly selects a typical area with a size of 500 ⁇ 500 ⁇ 500 pixels.
  • Quantitative analysis parameters the analysis of the size, volume, quantity and pore size of organic matter is completed by image J software. The pore size of organic matter is expressed in terms of equivalent diameter.
  • the specific operation process is as follows: preprocess the image to remove artifacts and then perform slice segmentation, output as a binary image (all black), convert the binary image to an octal image (as shown in Figure 2) and perform threshold segmentation, with the gray value range from 0 to 255, where 0 means black with the lowest brightness, and 255 means pure white with the highest brightness.
  • the selection of the threshold value adopts the global threshold value method to conduct experimental analysis on the image to be processed, and use the observation histogram to select.
  • the pores have no absorption of X-rays, the gray value is the smallest, the soil mineral absorption is the largest, the gray value is larger, and the organic matter is medium. Between the two, build a histogram according to the different gray values of each component.
  • the histogram will have 2 peaks.
  • the gray value of the middle trough of the 2 peaks can be selected as the threshold, which distinguishes the segmentation threshold of organic matter and minerals.
  • An example of analysis is shown in Figure 3. Appropriate smoothing can be performed before conversion to make the boundary contour clear and improve the signal-to-noise ratio.
  • the organic matter components and soil minerals can be dyed to enhance the visual contrast between the two ( Figure 4).
  • a 3D analysis tool was used to reconstruct 1508 slices to restore the original appearance of the soil granular organic matter.
  • the volume ratio and the quantity per unit volume were quantitatively analyzed, and the results are shown in Table 1. Among them, the volume ratio is the ratio of organic matter to the sampling volume (500 ⁇ 500 ⁇ 500).
  • the method of micro-CT technology to study soil particulate organic matter can learn from the description of soil pore morphology, and the morphology factor (or pore) of particulate organic matter is expressed as follows:
  • A is the actual surface area of the particulate organic matter.
  • the pore size of soil granular organic matter is expressed by equivalent diameter.
  • the granular organic pores are divided into four parts, which are ultramicro pores ( ⁇ 5 ⁇ m), micropores (5-30 ⁇ m), mesopores (30-80 ⁇ m) and macropores (>80 ⁇ m).
  • ultramicro pores ⁇ 5 ⁇ m
  • micropores 5-30 ⁇ m
  • mesopores (30-80 ⁇ m)
  • macropores >80 ⁇ m.
  • the porosity distribution characteristics of granular organic matter are shown in Table 3.
  • the porosity is the ratio of the pore volume to the sampling volume (500 ⁇ 500 ⁇ 500).
  • the above method can continue to quantitatively determine the spatial structure of iPOM (250-2000 ⁇ m), fPOM (53-250 ⁇ m) and iPOM (53-250 ⁇ m).
  • the invention provides an effective method for the quantitative determination of the spatial structure of soil granular organic matter (morphological characteristics, organic matter quantity, volume ratio and volume distribution, pore size distribution and other parameters), which lays a foundation for the in-depth study of soil science, especially soil organic matter
  • the basic method The basic method.

Landscapes

  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Dispersion Chemistry (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Pulmonology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Theoretical Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)

Abstract

L'invention concerne un procédé de mesure quantitative de la structure spatiale de matière organique particulaire du sol. Le procédé comprend : premièrement, l'extraction d'une matière organique particulaire dans le sol au moyen d'une classification par tamisage humide et d'une extraction de densité ; puis la réalisation d'un balayage d'image sur la matière organique particulaire au moyen d'une technologie micro-CT ; la réalisation successive d'une suppression d'artéfacts, d'un calcul de seuil, d'une segmentation d'image et d'une reconstruction tridimensionnelle sur une image obtenue après le balayage ; et la mesure quantitative des caractéristiques morphologiques, de la quantité, du rapport de volume, de la distribution de volume, de la distribution de taille de pore, etc. de la matière organique du sol.
PCT/CN2019/080778 2019-03-14 2019-04-01 Procédé de mesure quantitative d'une structure spatiale de matière organique particulaire du sol Ceased WO2020181594A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2019434881A AU2019434881A1 (en) 2019-03-14 2019-04-01 Method for quantitatively measuring spatial structure of soil particulate organic matter

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910192643.3A CN109959602B (zh) 2019-03-14 2019-03-14 一种定量测定土壤颗粒态有机质空间结构的方法
CN201910192643.3 2019-03-14

Publications (1)

Publication Number Publication Date
WO2020181594A1 true WO2020181594A1 (fr) 2020-09-17

Family

ID=67024248

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/080778 Ceased WO2020181594A1 (fr) 2019-03-14 2019-04-01 Procédé de mesure quantitative d'une structure spatiale de matière organique particulaire du sol

Country Status (3)

Country Link
CN (1) CN109959602B (fr)
AU (2) AU2019434881A1 (fr)
WO (1) WO2020181594A1 (fr)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113447419A (zh) * 2021-06-28 2021-09-28 哈尔滨工业大学 一种多孔介质连通孔隙结构单元的划分系统
CN114112975A (zh) * 2021-11-23 2022-03-01 广东省农业科学院农业资源与环境研究所 一种基于同步辐射红外显微成像的土壤分析方法
CN114184627A (zh) * 2021-10-20 2022-03-15 广东工业大学 一种深海沉积物中微塑料和宏塑料的无损探测分析方法
CN115049566A (zh) * 2022-08-15 2022-09-13 聊城扬帆田一机械有限公司 一种平板夯激振模式智能调节系统
CN116060432A (zh) * 2023-02-21 2023-05-05 东北师范大学 一种物理分离土壤不同有机质组成的方法
CN116148143A (zh) * 2023-03-07 2023-05-23 重庆茂侨科技有限公司 粗集料级配、针片状含量和不规则颗粒含量检测方法
CN118521657A (zh) * 2024-04-09 2024-08-20 中国科学院南京土壤研究所 一种基于ct图像将颗粒有机质分为新鲜pom和旧pom的方法
CN118706874A (zh) * 2024-07-12 2024-09-27 江西农业大学 基于ct扫描技术量化土壤孔隙与作物根系空间关系的方法
CN118886343A (zh) * 2024-10-08 2024-11-01 吉林农业大学 土壤团聚体三维孔隙结构动态演化过程的生成方法和设备

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111076992B (zh) * 2019-11-21 2024-05-24 浙江华才检测技术有限公司 一种土壤分离检测系统
CN113670787A (zh) * 2020-05-14 2021-11-19 中国石油天然气股份有限公司 泥页岩中有机质与其他矿物的微孔与介孔孔径测试方法
CN114966120A (zh) * 2022-01-05 2022-08-30 甘肃农业大学 一种基于扫描电镜定量比较农学试验处理间土壤团聚体特征的方法
CN117218437B (zh) * 2023-09-18 2024-03-01 中国科学院南京土壤研究所 一种ct技术结合机器学习原位定量土壤颗粒有机质的方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998051786A1 (fr) * 1997-05-13 1998-11-19 Advanced Biological Services, Inc. Site de reaction destine a des microorganismes utilises pour biodegrader des contaminants, procede d'utilisation associe
CN105761241A (zh) * 2016-01-25 2016-07-13 中国水利水电科学研究院 一种基于ct扫描图像的土壤大孔隙空间结构确定方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998051786A1 (fr) * 1997-05-13 1998-11-19 Advanced Biological Services, Inc. Site de reaction destine a des microorganismes utilises pour biodegrader des contaminants, procede d'utilisation associe
CN105761241A (zh) * 2016-01-25 2016-07-13 中国水利水电科学研究院 一种基于ct扫描图像的土壤大孔隙空间结构确定方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, XINGHUA: "The Characteristics of Particulate Organic Matters and Their Adsorption of Antibiotics in the Coastal Soils and Sediments in the Yellow River Delta", CNKI CHINA DOCTORAL DISSERTATIONS FULL-TEXT DATABASE; ENGINEERING SCIENCE AND TECHNOLOGY I, 15 November 2018 (2018-11-15), ISSN: 1674-022X, DOI: 20190929092239X *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113447419B (zh) * 2021-06-28 2022-10-25 哈尔滨工业大学 一种多孔介质连通孔隙结构单元的划分系统
CN113447419A (zh) * 2021-06-28 2021-09-28 哈尔滨工业大学 一种多孔介质连通孔隙结构单元的划分系统
CN114184627B (zh) * 2021-10-20 2023-12-01 广东工业大学 一种深海沉积物中微塑料和宏塑料的无损探测分析方法
CN114184627A (zh) * 2021-10-20 2022-03-15 广东工业大学 一种深海沉积物中微塑料和宏塑料的无损探测分析方法
CN114112975A (zh) * 2021-11-23 2022-03-01 广东省农业科学院农业资源与环境研究所 一种基于同步辐射红外显微成像的土壤分析方法
CN115049566A (zh) * 2022-08-15 2022-09-13 聊城扬帆田一机械有限公司 一种平板夯激振模式智能调节系统
CN115049566B (zh) * 2022-08-15 2022-10-25 聊城扬帆田一机械有限公司 一种平板夯激振模式智能调节系统
CN116060432A (zh) * 2023-02-21 2023-05-05 东北师范大学 一种物理分离土壤不同有机质组成的方法
CN116148143A (zh) * 2023-03-07 2023-05-23 重庆茂侨科技有限公司 粗集料级配、针片状含量和不规则颗粒含量检测方法
CN116148143B (zh) * 2023-03-07 2024-01-12 重庆茂侨科技有限公司 粗集料级配、针片状含量和不规则颗粒含量检测方法
CN118521657A (zh) * 2024-04-09 2024-08-20 中国科学院南京土壤研究所 一种基于ct图像将颗粒有机质分为新鲜pom和旧pom的方法
CN118706874A (zh) * 2024-07-12 2024-09-27 江西农业大学 基于ct扫描技术量化土壤孔隙与作物根系空间关系的方法
CN118886343A (zh) * 2024-10-08 2024-11-01 吉林农业大学 土壤团聚体三维孔隙结构动态演化过程的生成方法和设备

Also Published As

Publication number Publication date
AU2019101789A4 (en) 2021-04-15
AU2019434881A1 (en) 2020-12-10
CN109959602A (zh) 2019-07-02
CN109959602B (zh) 2020-11-20

Similar Documents

Publication Publication Date Title
WO2020181594A1 (fr) Procédé de mesure quantitative d'une structure spatiale de matière organique particulaire du sol
Wang et al. 3D image segmentation for analysis of multisize particles in a packed particle bed
Xie et al. 3D size and shape characterization of natural sand particles using 2D image analysis
CN110070601A (zh) 显微图像重建和分割的远程深度学习的方法、装置和系统
CN102494976B (zh) 一种超细晶粒钢晶粒的自动测量及其形态分类方法
CN106546521A (zh) 一种基于ct扫描技术量化土壤大孔隙空间网络结构的方法
CN110021030A (zh) 一种岩土体材料数字图像的分割阈值确定方法
WO2012139313A1 (fr) Procédé d'identification d'un motif de cellules cancéreuses par imagerie microscopique par rayons x mous
CN113390905B (zh) 一种基于ct扫描技术量化土壤团聚体孔隙空间结构方法
Ganju et al. Quantification of displacement and particle crushing around a penetrometer tip
CN110441342A (zh) 一种精确表征晶体三维取向和晶体学取向的方法
CN117554396A (zh) 土壤团聚体原位分离方法及装置
Hagenmuller et al. Motion of dust particles in dry snow under temperature gradient metamorphism
CN108061697B (zh) 土体三维孔隙率计算方法
CN117218437B (zh) 一种ct技术结合机器学习原位定量土壤颗粒有机质的方法
Vicente et al. Characterization (two-dimensional− three-dimensional) of ceramic microfiltration membrane by synchrotron radiation: new and abraded membranes
CN103824224A (zh) 一种基于明暗恢复形状的水果大小分级方法
Dong et al. Quantification of the 3D morphology of the bone cell network from synchrotron micro-CT images
CN107764797A (zh) 一种基于低秩张量算法的拉曼光谱图像数据预处理方法
CN106023223A (zh) 柑橘果实大小描述及分级方法
Gong et al. Characterization of micro-scale pore structure and permeability simulation of peat soil based on 2D/3D X-ray computed tomography images
CN118706874A (zh) 基于ct扫描技术量化土壤孔隙与作物根系空间关系的方法
Pallua et al. Application of micro-computed tomography to microstructure studies of the medicinal fungus Hericium coralloides
TWI455039B (zh) Calculation method of average particle size distribution of batch coke
Phromsuwan et al. Quantitative analysis of X-ray lithographic pores by SEM image processing

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19919157

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019434881

Country of ref document: AU

Date of ref document: 20190401

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19919157

Country of ref document: EP

Kind code of ref document: A1